bolt
フロントエンド(再レンダリング削減、メモ化、lazy loading)とバックエンド(N+1修正、インデックス、キャッシュ、非同期処理)両面のパフォーマンス改善。速度向上、最適化が必要な時に使用。
Discover tools framework internals skills, compare source repositories and read the instructions for each workflow.
フロントエンド(再レンダリング削減、メモ化、lazy loading)とバックエンド(N+1修正、インデックス、キャッシュ、非同期処理)両面のパフォーマンス改善。速度向上、最適化が必要な時に使用。
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google...
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and...
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error...
Lightweight 4-gate pre-dev workflow for small features (
Token-optimized morning/nightly routine using caching and selective execution
Architecture decision guides for Kailash SDK including framework selection (Core SDK vs DataFlow vs Nexus vs Kaizen), runtime selection (Async vs Sync), database selection (PostgreSQL vs SQLite), node selection...
Universal Python development guidelines and best practices
Use when optimizing Swift code performance, reducing memory usage, improving runtime efficiency, dealing with COW, ARC overhead, generics specialization, or collection optimization
Logic optimization and image-based code tasks with Codex CLI in --yolo mode. Use when (1) optimizing code logic or algorithms, (2) processing image inputs (screenshots, diagrams, mockups) to generate or modify...
Copilot agent that assists with performance analysis, bottleneck detection, optimization strategies, and benchmarking Trigger terms: performance optimization, performance tuning, profiling, benchmark, bottlene...
Guide for sp.h, a single-header C standard library replacement. You must use this guide when using or discussing sp.h in any capacity.
INVOKE for calculations and data processing. Produces executable code instead of mental math. Use when precision matters or numbers are complex. Triggers: calculations, numerical analysis, data transformations,...
Edge deployment strategies including mobile optimization, embedded systems, TFLite, Core ML, and resource-constrained inference.
AI hardware accelerators including GPUs, TPUs, custom silicon, and hardware-aware optimization strategies for ML workloads.
Applies AI-first development patterns for 10-100x productivity through specification-driven generation and leverage multiplication. Use when developing features with AI assistance or optimizing development work...
Bit manipulation tricks and techniques for solving problems efficiently using binary operations and XOR properties.
Python 3.12+の最新機能とベストプラクティスを活用したコード実装スキル
Master Python optimization techniques, profiling, memory management, and high-performance computing
Enterprise Claude Code context window optimization with 2025 best practices: aggressive clearing, memory file management, MCP optimization, strategic chunking, and quality-over-quantity principles for 200K toke...
Techniques for creating and adapting payloads for various exploitation scenarios, target environments, and evasion requirements
Explain Cairo ownership, moves, Copy/Drop/Destruct traits, and scope-based destruction; use when a request involves use-after-move errors, copying values, or ownership semantics in Cairo.
Explain Cairo method syntax, traits/impl blocks, `self` parameter forms, and associated functions; use when a request involves defining or calling methods on structs in Cairo.
Explain Cairo procedural macros implemented in Rust (inline/attribute/derive), project setup, and TokenStream/ProcMacroResult; use when a request involves writing or using procedural macros in Cairo.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.